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1. S. H. Giordano, “Update on locally advanced breast cancer,” Oncologist 8, 521530 (2003).
2. Guidelines for the Management of Breast Cancer, 31st ed., EMRO Technical Publications Series (World Health Organization, Alexandria, Egypt, 2006), p. 44.
3. J. H. Youk, E.-K. Kim, M. J. Kim, J. Y. Lee, and K. K. Oh, “Missed breast cancers at US-guided core needle biopsy: How to reduce them,” Radiographics 27, 7994 (2007).
4. M. Insana and M. Oelze, “Advanced ultrasonic imaging techniques for breast cancer research,” Emerging Technologies in Breast Imaging and Mammography (American Scientific Publishers, Valencia, CA, 2006).
5. M. L. Oelze, W. D. O’ Brien, J. P. Blue, and J. F. Zachary, “Differentiation and characterization of rat mammary fibroadenomas and 4T1 mouse carcinomas using quantitative ultrasound imaging,” IEEE Trans. Med. Imaging 23, 764771 (2004).
6. E. J. Feleppa, J. Mamou, C. R. Porter, and J. Machi, “Quantitative ultrasound in cancer imaging,” Semin. Oncol. 38, 136150 (2011).
7. M. L. Oelze and J. F. Zachary, “Examination of cancer in mouse models using high-frequency quantitative ultrasound,” Ultrasound Med. Biol. 32, 16391648 (2006).
8. H. Nasief, I. Rosado-Mendez, J. Zagzebski, and T. Hall, “Quantitative ultrasound as an aid to differentiate benign from malignant breast masses,” in AIUM (American Institute of Ultrasound in Medicine) Annual Convention, New York, NY, 2013.
9. F. L. Lizzi, M. Ostromogilsky, E. J. Feleppa, M. C. Rorke, and M. M. Yaremko, “Relationship of ultrasonic spectral parameters to features of tissue microstructure,” IEEE Trans. Ultrason. Ferroelectr. Freq. Control 34, 319329 (1987).
10. D. J. Coleman, F. L. Lizzi, R. H. Silverman, L. Helson, J. H. Torpey, and M. J. Rondeau, “A model for acoustic characterization of intraocular tumors,” Invest. Ophthalmol. Visual Sci. 26, 545550 (1985).
11. E. J. Feleppa, A. Kalisz, J. B. Sokil-Melgar, F. L. Lizzi, A. L. Rosado, M. C. Shao, W. R. Fair, M. S. Cookson, V. E. Reuter, and W. D. W. Heston, “Typing of prostate tissue by ultrasonic spectrum analysis,” IEEE Trans. Ultrason. Ferroelectr. Freq. Control 43, 609619 (1996).
12. J. Mamou, A. Coron, M. Hata, J. Machi, E. Yanagihara, P. Laugier, and E. J. Feleppa, “Three-dimensional high-frequency characterization of cancerous lymph nodes,” Ultrasound Med. Biol. 36, 361375 (2010).
13. M. Yang, T. M. Krueger, J. G. Miller, and M. R. Holland, “Characterization of anisotropic myocardial backscatter using spectral slope, intercept and midband fit parameters,” Ultrason. Imaging 29, 122134 (2007).
14. F. L. Lizzi, M. Astor, T. Liu, C. Deng, D. J. Coleman, and R. H. Silverman, “Ultrasonic spectrum analysis for tissue assays and therapy evaluation,” Int. J. Imaging Syst. Technol. 8, 310 (1997).<3::AID-IMA2>3.0.CO;2-E
15. R. M. Vlad et al., “Quantitative ultrasound characterization of responses to radiotherapy in cancer mouse models,” Clin. Cancer Res. 15(6), 20672074 (2009).
16. J. Lee, R. Karshafian, N. Papanicolau, A. Giles, M. C. Kolios, and G. J. Czarnota, “Quantitative ultrasound for the monitoring of novel microbubble and ultrasound radiosensitization,” Ultrasound Med. Biol. 38, 12121221 (2012).
17. L. L. Fellingham and F. G. Sommer, “Ultrasonic characterization of tissue structure in the in vivo human liver and spleen,” IEEE Trans. Sonics Ultrason. 31, 418428 (1984).
18. K. Suzuki, N. Hayashi, Y. Sasaki, M. Kono, A. Kasahara, Y. Imai, H. Fusamoto, and T. Kamada, “Evaluation of structural change in diffuse liver disease with frequency domain analysis of ultrasound,” Hepatology 17, 10411046 (1993).
19. C. B. Machado, W. C. Pereira, M. Meziri, and P. Laugier, “Characterization of in vitro healthy and pathological human liver tissue periodicity using backscattered ultrasound signals,” Ultrasound Med. Biol. 32, 649657 (2006).
20. U. Abeyratne and X. Tang, “Ultrasound scatter-spacing based diagnosis of focal diseases of the liver,” Biomed. Signal Process. Control 2, 915 (2007).
21. Y. Bige, Z. Hanfeng, and W. Rong, “Analysis of microstructural alterations of normal and pathological breast tissue in vivo using the AR cepstrum,” Ultrasonics 44, 211215 (2006).
22. K. A. Wear, R. F. Wagner, M. F. Insana, and T. J. Hall, “Application of autoregressive spectral analysis to cepstral estimation of mean scatterer spacing,” IEEE Trans. Ultrason. Ferroelectr. Freq. Control 40, 5058 (1993).
23. X. Tang and U. R. Abeyratne, “Wavelet transforms in estimating scatterer spacing from ultrasound echoes,” Ultrasonics 38, 688692 (2000).
24. J. Tsao and G.-S. Jiang, “Mean scatterer spacing estimation using wavelet spectrum,” Proc.-IEEE Ultrason. Symp. 3, 20902093 (2004).
25. T. Varghese and K. D. Donohue, “Characterization of tissue microstructure scatterer distribution with spectral correlation,” Ultrason. Imaging 15, 238254 (1993).
26. Y.-Y. Liao, P.-H. Tsui, C.-H. Li, K.-J. Chang, W.-H. Kuo, C.-C. Chang, and C.-K. Yeh, “Classification of scattering media within benign and malignant breast tumors based on ultrasound texture-feature-based and Nakagami-parameter images,” Med. Phys. 38, 21982207 (2011).
27. S. H. Kim, B. K. Seo, J. Lee, S. J. Kim, K. R. Cho, K. Y. Lee, B.-K. Je, H. Y. Kim, Y.-S. Kim, and J.-H. Lee, “Correlation of ultrasound findings with histology, tumor grade, and biological markers in breast cancer,” Acta Oncol. 47, 15311538 (2008).
28. H. J. Bloom and W. W. Richardson, “Histological grading and prognosis in breast cancer: A study of 1409 cases of which 359 have been followed for 15 years,” Br. J. Cancer 11, 359377 (1957).
29. K. A. Topp, J. F. Zachary, and W. D. O’Brien, “Quantifying B-mode images of in vivo rat mammary tumors by the frequency dependence of backscatter,” J. Ultrasound Med. 20, 605612 (2001).
30. E. L. Madsen, J. A. Zagzebski, R. A. Banjavie, and R. E. Jutila, “Tissue mimicking materials for ultrasound phantoms,” Med. Phys. 5, 391394 (1978).
31. S. M. Kay, Modern Spectral Estimation: Theory and Application (Prentice-Hall, Englewood Cliffs, NJ, 1998).
32. C. Li, N. Duric, P. Littrup, and L. Huang, “In vivo breast sound-speed imaging with ultrasound tomography,” Ultrasound Med. Biol. 35, 16151628 (2009).
33. M. F. Insana, R. F. Wagner, D. G. Brown, and T. J. Hall, “Describing small-scale structure in random media using pulse-echo ultrasound,” J. Acoust. Soc. Am. 87, 179192 (1990).
34. L. X. Yao, J. A. Zagzebski, and E. L. Madsen, “Backscatter coefficient measurements using a reference phantom to extract depth-dependent instrumentation factors,” Ultrason. Imaging 12, 5870 (1990).
35. J. J. Anderson, M. Herd, M. R. King, A. Haak, Z. T. Hafez, J. Song, M. L. Oelze, E. L. Madsen, J. Zagzebski, W. D. O’Brien, and T. J. Hall, “Interlaboratory comparison of backscatter coefficient estimates for tissue-mimicking phantoms,” Ultrason. Imaging 32, 4864 (2010).
36. F. T. D’Astous and F. S. Foster, “Frequency dependence of ultrasound attenuation and backscatter in breast tissue,” Ultrasound Med. Biol. 12(10), 795808 (1986).
37. N. Duric, P. Littrup, A. Babkin, D. Chambers, S. Azevedo, A. Kalinin, R. Pevzner, M. Tokarev, E. Holsapple, O. Rama, and R. Duncan, “Development of ultrasound tomography for breast imaging: Technical assessment,” Med. Phys. 32, 13751386 (2005).
38. M. F. Insana and T. J. Hall, “Parametric ultrasound imaging from backscatter coefficient measurements: Image formation and interpretation,” Ultrason. Imaging 12, 245267 (1990).
39. V. C. Anderson, “Sound scattering from a fluid sphere,” J. Acoust. Soc. Am. 22, 426431 (1950).
40. R. M. Haralick, K. Shanmugam, and I. Dinstein, “Textural features for image classification,” IEEE Trans. Syst. Man Cybern. 3, 610621 (1973).
41. Y. Labyed and T. A. Bigelow, “A theoretical comparison of attenuation measurement techniques from backscattered ultrasound echoes,” J. Acoust. Soc. Am. 129, 23162324 (2011).
42. P. D. Edmonds, C. L. Mortensen, J. R. Hill, S. K. Holland, J. F. Jensen, P. Schattner, and A. D. Valdes, “Ultrasound tissue characterization of breast biopsy specimens,” Ultrason. Imaging 13, 162185 (1991).

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Tumor grading is an important part of breast cancer diagnosis and currently requires biopsy as its standard. Here, the authors investigate quantitative ultrasound parameters in locally advanced breast cancers that can potentially separate tumors from normal breast tissue and differentiate tumor grades.

Ultrasound images and radiofrequency data from 42 locally advanced breast cancer patients were acquired and analyzed. Parameters related to the linear regression of the power spectrum—midband fit, slope, and 0-MHz-intercept—were determined from breast tumors and normal breast tissues. Mean scatterer spacing was estimated from the spectral autocorrelation, and the effective scatterer diameter and effective acoustic concentration were estimated from the Gaussian form factor. Parametric maps of each quantitative ultrasound parameter were constructed from the gated radiofrequency segments in tumor and normal tissue regions of interest. In addition to the mean values of the parametric maps, higher order statistical features, computed from gray-level co-occurrence matrices were also determined and used for characterization. Finally, linear and quadratic discriminant analyses were performed using combinations of quantitative ultrasound parameters to classify breast tissues.

Quantitative ultrasound parameters were found to be statistically different between tumor and normal tissue (p < 0.05). The combination of effective acoustic concentration and mean scatterer spacing could separate tumor from normal tissue with 82% accuracy, while the addition of effective scatterer diameter to the combination did not provide significant improvement (83% accuracy). Furthermore, the two advanced parameters, including effective scatterer diameter and mean scatterer spacing, were found to be statistically differentiating among grade I, II, and III tumors (p = 0.014 for scatterer spacing, p = 0.035 for effective scatterer diameter). The separation of the tumor grades further improved when the textural features of the effective scatterer diameter parametric map were combined with the mean value of the map (p = 0.004).

Overall, the binary classification results (tumor versus normal tissue) were more promising than tumor grade assessment. Combinations of advanced parameters can further improve the separation of tumors from normal tissue compared to the use of linear regression parameters. While the linear regression parameters were sufficient for characterizing breast tumors and normal breast tissues, advanced parameters and their textural features were required to better characterize tumor subtypes.


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